from bluepyopt.ephys.responses import TimeVoltageResponse
from bluepyparallel import init_parallel_factory
import yaml
from functools import partial

from pathlib import Path
import matplotlib.pyplot as plt
import pickle

# from emodel_generalisation.utils import plot_traces
from emodel_generalisation.model.modifiers import synth_soma, synth_axon
from emodel_generalisation.mcmc import load_chains
from emodel_generalisation.mcmc import save_selected_emodels
import logging
from itertools import cycle
import json
from matplotlib.backends.backend_pdf import PdfPages
import pandas as pd
from emodel_generalisation.model.access_point import AccessPoint
from emodel_generalisation.model.evaluation import feature_evaluation
from emodel_generalisation.utils import get_combo_hash
from datareuse import Reuse
import extra_features


def plot_traces(trace_df, trace_path="traces", pdf_filename="traces.pdf"):
    """Plot traces from df, with highlighs on rows with trace_highlight = True.

    Args:
        trace_df (DataFrame): contains list of combos with traces to plot
        trace_path (str): path to folder with traces in .pkl
        pdf_filename (str): name of pdf to save
    """
    COLORS = cycle(["r"] + [f"C{i}" for i in range(10)])
    trace_df = trace_df.copy()  # prevents annyoing panda warnings
    if "trace_highlight" not in trace_df.columns:
        trace_df["trace_highlight"] = True
    for index in trace_df.index:
        if trace_df.loc[index, "trace_highlight"]:
            c = next(COLORS)

        if "trace_data" in trace_df.columns:
            trace_path = trace_df.loc[index, "trace_data"]
        else:
            combo_hash = get_combo_hash(trace_df.loc[index])
            trace_path = Path(trace_path) / ("trace_id_" + str(combo_hash) + ".pkl")

        with open(trace_path, "rb") as f:
            trace = pickle.load(f)
            if isinstance(trace, list):
                trace = trace[1]  # newer version the response are here
            for protocol, response in trace.items():
                if isinstance(response, TimeVoltageResponse):
                    if trace_df.loc[index, "trace_highlight"]:
                        label = trace_df.loc[index, "name"]
                        lw = 0.5
                        zorder = 1
                    else:
                        label = None
                        c = "0.5"
                        lw = 0.5
                        zorder = -1

                    plt.figure(protocol, figsize=(30, 7))
                    # plt.gca().set_xlim(1500, 1700)
                    # if protocol == 'Step_ReboundBurst_burst.soma.ina':
                    #    plt.gca().set_ylim(-60000, 0)
                    plt.plot(
                        response["time"],
                        response["voltage"],
                        label=label,
                        c=c,
                        lw=lw,
                        zorder=zorder,
                    )

    with PdfPages(pdf_filename) as pdf:
        for fig_id in plt.get_fignums():
            fig = plt.figure(fig_id)
            plt.legend(loc="best")
            plt.suptitle(fig.get_label())
            pdf.savefig()
            plt.close()


if __name__ == "__main__":
    parallel_factory = init_parallel_factory("multiprocessing")
    # parallel_factory = init_parallel_factory("dask_dataframe")
    emodel = "simplest"
    logger = logging.getLogger()
    v = 1
    logging.basicConfig(
        level=(logging.WARNING, logging.INFO, logging.DEBUG)[v],
        handlers=[logging.StreamHandler()],
    )
    logger.setLevel((logging.WARNING, logging.INFO, logging.DEBUG)[v])
    """
    mcmc_df = load_chains("run_df.csv")
    mcmc_df = mcmc_df[mcmc_df.cost < 2]
    mask = (
        # (mcmc_df["features"]["Step_ReboundBurst_burst.soma.v.burst_runaway"] < 0.05)
        # & (mcmc_df["features"]["Step_ReboundBurst_burst.soma.v.time_to_last_spike"] > 20000)
        mcmc_df["features"]["Step_ReboundBurst_burst.soma.v.all_burst_number"]
        < 20
    )
    mcmc_df = mcmc_df[mask]  # .sort_values(by="cost")
    print(mcmc_df)
    mcmc_df = mcmc_df.sample(1).reset_index()
    save_selected_emodels(mcmc_df, mcmc_df.index, emodel=emodel, final_path="final.json")
    """

    access_point = AccessPoint(
        emodel_dir=".", recipes_path="config/recipes.json", final_path="final.json", with_seeds=True
    )
    exemplar_data = yaml.safe_load(open("exemplar_data.yaml"))
    access_point.morph_path = exemplar_data["paths"]["all"]

    access_point.settings["morph_modifiers"] = [
        partial(synth_soma, params=exemplar_data["soma"], scale=1.0),
        partial(synth_axon, params=exemplar_data["ais"]["popt"], scale=1.0),
    ]

    df = pd.DataFrame()
    df = pd.read_csv("morphologies.csv")
    df["emodel"] = "simplest_0"
    print(df)
    Path("traces").mkdir(exist_ok=True)
    with Reuse("eval_traces.csv") as reuse:
        df = reuse(
            feature_evaluation,
            df,
            access_point,
            parallel_factory=parallel_factory,
            trace_data_path="traces",
            # record_ions_and_currents=True,
        )
    plot_traces(df)
    plt.close("all")
